Relevant bibliographies by topics / No-reference metrics

Academic literature on the topic 'No-reference metrics'

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Published: 9 March 2023

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Journal articles on the topic "No-reference metrics"

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Torres Vega, Maria, Vittorio Sguazzo, Decebal Constantin Mocanu, and Antonio Liotta. "An experimental survey of no-reference video quality assessment methods." International Journal of Pervasive Computing and Communications 12, no. 1 (April 4, 2016): 66–86. http://dx.doi.org/10.1108/ijpcc-01-2016-0008.

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Purpose The Video Quality Metric (VQM) is one of the most used objective methods to assess video quality, because of its high correlation with the human visual system (HVS). VQM is, however, not viable in real-time deployments such as mobile streaming, not only due to its high computational demands but also because, as a Full Reference (FR) metric, it requires both the original video and its impaired counterpart. In contrast, No Reference (NR) objective algorithms operate directly on the impaired video and are considerably faster but loose out in accuracy. The purpose of this paper is to study how differently NR metrics perform in the presence of network impairments. Design/methodology/approach The authors assess eight NR metrics, alongside a lightweight FR metric, using VQM as benchmark in a self-developed network-impaired video data set. This paper covers a range of methods, a diverse set of video types and encoding conditions and a variety of network impairment test-cases. Findings The authors show the extent by which packet loss affects different video types, correlating the accuracy of NR metrics to the FR benchmark. This paper helps identifying the conditions under which simple metrics may be used effectively and indicates an avenue to control the quality of streaming systems. Originality/value Most studies in literature have focused on assessing streams that are either unaffected by the network (e.g. looking at the effects of video compression algorithms) or are affected by synthetic network impairments (i.e. via simulated network conditions). The authors show that when streams are affected by real network conditions, assessing Quality of Experience becomes even harder, as the existing metrics perform poorly.
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Pinson, Margaret H., Philip J. Corriveau, Mikołaj Leszczuk, and Michael Colligan. "Open Software Framework for Collaborative Development of No Reference Image and Video Quality Metrics." Electronic Imaging 2020, no. 11 (January 26, 2020): 92–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.11.hvei-092.

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This paper describes ongoing work within the video quality experts group (VQEG) to develop no-reference (NR) audiovisual video quality analysis (VQA) metrics. VQEG provides an open forum that encourages knowledge sharing and collaboration. The VQEG no-reference Metric (NORM) group’s goal is to develop open-source NR-VQA metrics that meet industry requirements for scope, accuracy, and capability. This paper presents industry specifications from discussions at VQEG face-to-face meetings among industry, academic, and government participants. This paper also announces an open software framework for collaborative development of NR image quality Analysis (IQA) and VQA metrics <ext-link ext-link-type="url" xlink:href="https://github.com/NTIA/NRMetricFramework"><https://github.com/NTIA/NRMetricFramework></ext-link>. This framework includes the support tools necessary to begin research and avoid common mistakes. VQEG’s goal is to produce a series of NR-VQA metrics with progressively improving scope and accuracy. This work draws upon and enables IQA metric research, as both use the human visual system to analyze the quality of audiovisual media on modern displays. Readers are invited to participate.
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Woodard, Jeffrey P., and Monica P. Carley-Spencer. "No-Reference Image Quality Metrics for Structural MRI." Neuroinformatics 4, no. 3 (2006): 243–62. http://dx.doi.org/10.1385/ni:4:3:243.

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Cao, Zhipeng, Zhenzhong Wei, and Guangjun Zhang. "A No-Reference Sharpness Metric Based on Structured Ringing for JPEG2000 Images." Advances in Optical Technologies 2014 (June 24, 2014): 1–13. http://dx.doi.org/10.1155/2014/295615.

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This work presents a no-reference image sharpness metric based on human blur perception for JPEG2000 compressed image. The metric mainly uses a ringing measure. And a blurring measure is used for compensation when the blur is so severe that ringing artifacts are concealed. We used the anisotropic diffusion for the preliminary ringing map and refined it by considering the property of ringing structure. The ringing detection of the proposed metric does not depend on edge detection, which is suitable for high degraded images. The characteristics of the ringing and blurring measures are analyzed and validated theoretically and experimentally. The performance of the proposed metric is tested and compared with that of some existing JPEG2000 sharpness metrics on three widely used databases. The experimental results show that the proposed metric is accurate and reliable in predicting the sharpness of JPEG2000 images.
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LE CALLET, P. "No Reference and Reduced Reference Video Quality Metrics for End to End QoS Monitoring." IEICE Transactions on Communications E89-B, no. 2 (February 1, 2006): 289–96. http://dx.doi.org/10.1093/ietcom/e89-b.2.289.

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Rubel, Andrii, Oleg Ieremeiev, Vladimir Lukin, Jarosław Fastowicz, and Krzysztof Okarma. "Combined No-Reference Image Quality Metrics for Visual Quality Assessment Optimized for Remote Sensing Images." Applied Sciences 12, no. 4 (February 14, 2022): 1986. http://dx.doi.org/10.3390/app12041986.

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No-reference image quality assessment is one of the most demanding areas of image analysis for many applications where the results of the analysis should be strongly correlated with the quality of an input image and the corresponding reference image is unavailable. One of the examples might be remote sensing since the transmission of such obtained images often requires the use of lossy compression and they are often distorted, e.g., by the presence of noise and blur. Since the practical usefulness of acquired and/or preprocessed images is directly related to their quality, there is a need for the development of reliable and adequate no-reference metrics that do not need any reference images. As the performance and universality of many existing metrics are quite limited, one of the possible solutions is the design and application of combined metrics. Several possible approaches to their composition have been previously proposed and successfully used for full-reference metrics. In the paper, three possible approaches to the development and optimization of no-reference combined metrics are investigated and verified for the dataset of images containing distortions typical for remote sensing. The proposed approach leads to good results, significantly improving the correlation of the obtained results with subjective quality scores.
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Wang, Hui, Xiaojuan Hu, Hui Xu, Shiyin Li, and Zhaolin Lu. "No-Reference Quality Assessment Method for Blurriness of SEM Micrographs with Multiple Texture." Scanning 2019 (June 2, 2019): 1–15. http://dx.doi.org/10.1155/2019/4271761.

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Scanning electron microscopy (SEM) plays an important role in the intuitive understanding of microstructures because it can provide ultrahigh magnification. Tens or hundreds of images are regularly generated and saved during a typical microscopy imaging process. Given the subjectivity of a microscopist’s focusing operation, blurriness is an important distortion that debases the quality of micrographs. The selection of high-quality micrographs using subjective methods is expensive and time-consuming. This study proposes a new no-reference quality assessment method for evaluating the blurriness of SEM micrographs. The human visual system is more sensitive to the distortions of cartoon components than to those of redundant textured components according to the Gestalt perception psychology and the entropy masking property. Micrographs are initially decomposed into cartoon and textured components. Then, the spectral and spatial sharpness maps of the cartoon components are extracted. One metric is calculated by combining the spatial and spectral sharpness maps of the cartoon components. The other metric is calculated on the basis of the edge of the maximum local variation map of the cartoon components. Finally, the two metrics are combined as the final metric. The objective scores generated using this method exhibit high correlation and consistency with the subjective scores.
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Liu, Xinwei, Marius Pedersen, and Christophe Charrier. "Performance evaluation of no-reference image quality metrics for face biometric images." Journal of Electronic Imaging 27, no. 02 (March 2, 2018): 1. http://dx.doi.org/10.1117/1.jei.27.2.023001.

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Gu, Ke, Guangtao Zhai, Xiaokang Yang, and Wenjun Zhang. "No-Reference Stereoscopic IQA Approach: From Nonlinear Effect to Parallax Compensation." Journal of Electrical and Computer Engineering 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/436031.

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The last decade has seen a booming of the applications of stereoscopic images/videos and the corresponding technologies, such as 3D modeling, reconstruction, and disparity estimation. However, only a very limited number of stereoscopic image quality assessment metrics was proposed through the years. In this paper, we propose a new no-reference stereoscopic image quality assessment algorithm based on the nonlinear additive model, ocular dominance model, and saliency based parallax compensation. Our studies using the Toyama database result in three valuable findings. First, quality of the stereoscopic image has a nonlinear relationship with a direct summation of two monoscopic image qualities. Second, it is a rational assumption that the right-eye response has the higher impact on the stereoscopic image quality, which is based on a sampling survey in the ocular dominance research. Third, the saliency based parallax compensation, resulted from different stereoscopic image contents, is considerably valid to improve the prediction performance of image quality metrics. Experimental results confirm that our proposed stereoscopic image quality assessment paradigm has superior prediction accuracy as compared to state-of-the-art competitors.
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Ye, Zhongchang, Xin Ye, and Zhonghua Zhao. "Hybrid No-Reference Quality Assessment for Surveillance Images." Information 13, no. 12 (December 16, 2022): 588. http://dx.doi.org/10.3390/info13120588.

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Intelligent video surveillance (IVS) technology is widely used in various security systems. However, quality degradation in surveillance images (SIs) may affect its performance on vision-based tasks, leading to the difficulties in the IVS system extracting valid information from SIs. In this paper, we propose a hybrid no-reference image quality assessment (NR IQA) model for SIs that can help to identify undesired distortions and provide useful guidelines for IVS technology. Specifically, we first extract two main types of quality-aware features: the low-level visual features related to various distortions, and the high-level semantic information, which is extracted by a state-of-the-art (SOTA) vision transformer backbone. Then, we fuse these two kinds of features into the final quality-aware feature vector, which is mapped into the quality index through the feature regression module. Our experimental results on two surveillance content quality databases demonstrate that the proposed model achieves the best performance compared to the SOTA on NR IQA metrics.
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Dissertations / Theses on the topic "No-reference metrics"

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MARINI, FABRIZIO. "Content based no-reference image quality metrics." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2012. http://hdl.handle.net/10281/29794.

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Images are playing a more and more important role in sharing, expressing, mining and exchanging information in our daily lives. Now we can all easily capture and share images anywhere and anytime. Since digital images are subject to a wide variety of distortions during acquisition, processing, compression, storage, transmission and reproduction; it becomes necessary to assess the Image Quality. In this thesis, starting from an organized overview of available Image Quality Assessment methods, some original contributions in the framework of No-reference image quality metrics are described.
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Silva, Alexandre Fieno da. "No-reference video quality assessment model based on artifact metrics for digital transmission applications." reponame:Repositório Institucional da UnB, 2017. http://repositorio.unb.br/handle/10482/24733.

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Tese (doutorado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2017.
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Um dos principais fatores para a redução da qualidade do conteúdo visual, em sistemas de imagem digital, são a presença de degradações introduzidas durante as etapas de processamento de sinais. Contudo, medir a qualidade de um vídeo implica em comparar direta ou indiretamente um vídeo de teste com o seu vídeo de referência. Na maioria das aplicações, os seres humanos são o meio mais confiável de estimar a qualidade de um vídeo. Embora mais confiáveis, estes métodos consomem tempo e são difíceis de incorporar em um serviço de controle de qualidade automatizado. Como alternativa, as métricas objectivas, ou seja, algoritmos, são geralmente usadas para estimar a qualidade de um vídeo automaticamente. Para desenvolver uma métrica objetiva é importante entender como as características perceptuais de um conjunto de artefatos estão relacionadas com suas forças físicas e com o incômodo percebido. Então, nós estudamos as características de diferentes tipos de artefatos comumente encontrados em vídeos comprimidos (ou seja, blocado, borrado e perda-de-pacotes) por meio de experimentos psicofísicos para medir independentemente a força e o incômodo desses artefatos, quando sozinhos ou combinados no vídeo. Nós analisamos os dados obtidos desses experimentos e propomos vários modelos de qualidade baseados nas combinações das forças perceptuais de artefatos individuais e suas interações. Inspirados pelos resultados experimentos, nós propomos uma métrica sem-referência baseada em características extraídas dos vídeos (por exemplo, informações DCT, a média da diferença absoluta entre blocos de uma imagem, variação da intensidade entre pixels vizinhos e atenção visual). Um modelo de regressão não-linear baseado em vetores de suporte (Support Vector Regression) é usado para combinar todas as características e estimar a qualidade do vídeo. Nossa métrica teve um desempenho muito melhor que as métricas de artefatos testadas e para algumas métricas com-referência (full-reference).
The main causes for the reducing of visual quality in digital imaging systems are the unwanted presence of degradations introduced during processing and transmission steps. However, measuring the quality of a video implies in a direct or indirect comparison between test video and reference video. In most applications, psycho-physical experiments with human subjects are the most reliable means of determining the quality of a video. Although more reliable, these methods are time consuming and difficult to incorporate into an automated quality control service. As an alternative, objective metrics, i.e. algorithms, are generally used to estimate video quality quality automatically. To develop an objective metric, it is important understand how the perceptual characteristics of a set of artifacts are related to their physical strengths and to the perceived annoyance. Then, to study the characteristics of different types of artifacts commonly found in compressed videos (i.e. blockiness, blurriness, and packet-loss) we performed six psychophysical experiments to independently measure the strength and overall annoyance of these artifact signals when presented alone or in combination. We analyzed the data from these experiments and proposed several models for the overall annoyance based on combinations of the perceptual strengths of the individual artifact signals and their interactions. Inspired by experimental results, we proposed a no-reference video quality metric based in several features extracted from the videos (e.g. DCT information, cross-correlation of sub-sampled images, average absolute differences between block image pixels, intensity variation between neighbouring pixels, and visual attention). A non-linear regression model using a support vector (SVR) technique is used to combine all features to obtain an overall quality estimate. Our metric performed better than the tested artifact metrics and for some full-reference metrics.
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Hettiarachchi, Don Lahiru Nirmal Manikka. "An Accelerated General Purpose No-Reference Image Quality Assessment Metric and an Image Fusion Technique." University of Dayton / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1470048998.

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Headlee, Jonathan Michael. "A No-reference Image Enhancement Quality Metric and Fusion Technique." University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1428755761.

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Morais, Dário Daniel Ribeiro. "A hybrid no-reference video quality metric for digital transmission applincatios." reponame:Repositório Institucional da UnB, 2017. http://repositorio.unb.br/handle/10482/23601.

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Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2017.
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Este trabalho visa desenvolver uma métrica híbrida de qualidade de vídeo sem referência para aplicações de transmissão digital, que leva em consideração três tipos de artefatos: perda de pacotes, blocado e borrado. As características desses artefatos são extraídas a partir das sequências de vídeo a fim de quantificar a força desses artefatos. A avaliação de perda de pacotes é dividida em 2 etapas: detecção e medição. As avaliações de blocado e borrado seguem referências da literatura. Depois de obter as características dos três tipos de artefatos, um processo de aprendizado de máquina (SVR) é utilizado para estimar a nota de qualidade prevista a partir das características extraídas. Os resultados obtidos com a métrica proposta foram comparados com os resultados obtidos com outras três métricas disponíveis na literatura (duas métricas NR de perda de pacotes e 1 métrica FR) e eles são promissores. A métrica proposta é cega, rápida e confiável para ser usada em cenários em tempo real.
This work aims to develop a hybrid no-reference video quality metric for digital transmission applications, which takes into account three types of artifacts: packet-loss, blockiness and bluriness. Features are extracted from the video sequences in order to quantity the strength of these three artifacts. The assessment of the packet-loss strength is performed in 2 stages: detection and measurement. The assessment of the strength of blockiness and blussiness follow references from literature. After obtaining the features from these three types of artifacts, a machine learning algorithm ( the support vector regression technique), is used to estimate the predicted quality score from the extracted features. The results obtained with the proposed metric were compared with the results obtained with three other metrics available in the literature (two NR packet-loss metrics and one FR metric). The proposed metric is blind, fast, and reliable to be used in real-time scenarios.
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Fiche, Cécile. "Repousser les limites de l'identification faciale en contexte de vidéo-surveillance." Thesis, Grenoble, 2012. http://www.theses.fr/2012GRENT005/document.

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Les systèmes d'identification de personnes basés sur le visage deviennent de plus en plus répandus et trouvent des applications très variées, en particulier dans le domaine de la vidéosurveillance. Or, dans ce contexte, les performances des algorithmes de reconnaissance faciale dépendent largement des conditions d'acquisition des images, en particulier lorsque la pose varie mais également parce que les méthodes d'acquisition elles mêmes peuvent introduire des artéfacts. On parle principalement ici de maladresse de mise au point pouvant entraîner du flou sur l'image ou bien d'erreurs liées à la compression et faisant apparaître des effets de blocs. Le travail réalisé au cours de la thèse porte donc sur la reconnaissance de visages à partir d'images acquises à l'aide de caméras de vidéosurveillance, présentant des artéfacts de flou ou de bloc ou bien des visages avec des poses variables. Nous proposons dans un premier temps une nouvelle approche permettant d'améliorer de façon significative la reconnaissance des visages avec un niveau de flou élevé ou présentant de forts effets de bloc. La méthode, à l'aide de métriques spécifiques, permet d'évaluer la qualité de l'image d'entrée et d'adapter en conséquence la base d'apprentissage des algorithmes de reconnaissance. Dans un second temps, nous nous sommes focalisés sur l'estimation de la pose du visage. En effet, il est généralement très difficile de reconnaître un visage lorsque celui-ci n'est pas de face et la plupart des algorithmes d'identification de visages considérés comme peu sensibles à ce paramètre nécessitent de connaître la pose pour atteindre un taux de reconnaissance intéressant en un temps relativement court. Nous avons donc développé une méthode d'estimation de la pose en nous basant sur des méthodes de reconnaissance récentes afin d'obtenir une estimation rapide et suffisante de ce paramètre
The person identification systems based on face recognition are becoming increasingly widespread and are being used in very diverse applications, particularly in the field of video surveillance. In this context, the performance of the facial recognition algorithms largely depends on the image acquisition context, especially because the pose can vary, but also because the acquisition methods themselves can introduce artifacts. The main issues are focus imprecision, which can lead to blurred images, or the errors related to compression, which can introduce the block artifact. The work done during the thesis focuses on facial recognition in images taken by video surveillance cameras, in cases where the images contain blur or block artifacts or show various poses. First, we are proposing a new approach that allows to significantly improve facial recognition in images with high blur levels or with strong block artifacts. The method, which makes use of specific noreference metrics, starts with the evaluation of the quality level of the input image and then adapts the training database of the recognition algorithms accordingly. Second, we have focused on the facial pose estimation. Normally, it is very difficult to recognize a face in an image taken from another viewpoint than the frontal one and the majority of facial identification algorithms which are robust to pose variation need to know the pose in order to achieve a satisfying recognition rate in a relatively short time. We have therefore developed a fast and satisfying pose estimation method based on recent recognition techniques
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Leite, Adriane de Oliveira. "Material complementar para o professor da rede SESI-SP de ensino : semelhança e software GeoGebra." Universidade Federal de São Carlos, 2015. https://repositorio.ufscar.br/handle/ufscar/7578.

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Não recebi financiamento
This research aims to propose activities for teachers using the Geogebra software, especially for teachers from the SESI-SP School Network in order to assist them in the teaching methodology, with teachers' work plan and, in addition, aiming to more significant and dynamic classes, in order to allow students reach their teaching and learning expectations, formulate valid arguments, make conjectures and justify their reasoning. The activities were applied by teachers of SESI-SP School Network to the students of 9th grade of elementary school, in anticipation of teaching and learning through “Similarity”, addressing Theorem of Thales, Metrics Relations in the Rectangle Triangle and Pythagoras Theorem. The results were analyzed and discussed, reporting the challenges and conclusions raised by the students during the activities while working with the Geogebra software and also based on the feedback provided by the teachers and the opinion of the analysts from SESI-SP School Network.
Esta pesquisa tem como objetivo principal propor atividades para os professores utilizando o software Geogebra, principalmente para os docentes da rede SESI-SP de Ensino, a fim de auxiliá-los na metodologia de ensino, no plano de trabalho, visando uma aula mais significativa e dinâmica, para que seus alunos atinjam as expectativas de ensino e aprendizagem, formulem argumentos válidos, façam conjecturas e justifiquem seus raciocínios. As atividades foram aplicadas por professores da rede SESI-SP de Ensino aos alunos do 9º ano do Ensino Fundamental, turma de 2014, na expectativa de ensino e aprendizagem de “Semelhança”, abordando Teorema de Tales, Relações Métricas no Triângulo Retângulo e Teorema de Pitágoras. Os resultados foram analisados e discutidos, relatando as dificuldades e conclusões apresentadas pelos alunos em desenvolver as atividades trabalhando com o software Geogebra, baseado nas devolutivas dos professores envolvidos e o parecer feito pelos analistas educacionais da Rede SESI-SP de Ensino.
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de, Silva Manawaduge Supun Samudika. "An Approach to Utilize a No-Reference Image Quality Metric and Fusion Technique for the Enhancement of Color Images." University of Dayton / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1470049079.

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Nordeng, Eirik Tørud. "Video metric measurements in an FPGA for use in objective no-reference video quality analysis." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for elektronikk og telekommunikasjon, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-22706.

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This thesis presents a way of performing objective video quality analyses in order to point out faults in the hardware of a video system that uses analogue video transmission technologies. The approach focuses on performing simple digital processing and analyses of the video data coherently using an FPGA. Several metrics that correlates with specific distortions are developed. These metrics give good indications of the state of the video system components. The algorithms are tested using MATLAB and mapped to an FPGA. The key components are implemented and verified in VHDL, and synthesized for an Altera Cyclone II FPGA. The thesis concludes that the proposed system has the ability to discover board-level faults in a video system that utilizes an FPGA and analogue video transmission. The system also has the ability to supplement external quality assessment systems in most cases, and function as a good alternative in cases where a quick and simple assessment of a video system is desired.
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Zach, Ondřej. "Nástroje pro měření kvality videosekvencí bez reference." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-219973.

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This diploma thesis deals with objective video quality assessments without reference. Some of the basics of video quality evaluation are described. Also some basic conditions for objective video quality metrics are introduced. The main focus of the thesis are no-reference approaches. Thesis tries to describe basic methods for seeking distortion in video. The difference between spatial-domain oriented and spectral-domain oriented metrics is analyzed. We also describe design of tool for measuring objective video quality in Matlab environment. We then designed and implemented a bit-stream oriented metric for estimation of the PSNR of H.264 coded sequences. Finally, we created a database of video sequences and we held objective tests. Results were compared with results from subjective measurements.
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Books on the topic "No-reference metrics"

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Soghier, Lamia, Katherine Pham, and Sara Rooney, eds. Reference Range Values for Pediatric Care. American Academy of Pediatrics, 2014. http://dx.doi.org/10.1542/9781581108545.

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Here’s the one place to look for normal values and related need-to-know data! Now you no longer have to search through multiple resources for reference ranges and other critical values you need to optimize patient assessment and management. The new Reference Range Values for Pediatric Care brings all the most vital range data - plus diverse clinical evaluation and calculation tools - all together in one concise, compact handbook. Indispensable pediatric reference ranges - right at your fingertips Custom-designed for today’s busy practitioners, this quick-access resource provides commonly used ranges and values spanning birth through adolescence. Data needed for management of preterm newborns and other neonates is highlighted throughout. Look here for practice-focused help with: - Blood pressure ranges - Body surface area calculation - Bone age metrics - Hematology values - Cerebrospinal fluid values - Lymphocyte subset counts - Clinical chemistry ranges - Thyroid function - Umbilical vein and artery catheterization measurements - Caloric intake values - And more! Also includes assessment and management tools you’ll use again and again Save time and simplify clinical problem-solving with a full set of easy-to-use tools from the AAP and other authoritative sources: - APGAR and Ballard newborn screening - Growth charts - Metric conversion tables - Pain scales - Blood pressure nomograms - Hyperbilirubinemia nomograms - Enternal formulas - GIR calculators - AAP immunization schedules - AAP periodicity schedule Drug administration and monitoring guidelines The handbook includes must-know basics on commonly used antibiotics and antiseizure medications - complete with recommended dosages and serum target levels.
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DE, Indrajit. Integrated Approach to Determination of Quality Metric for No-Reference Images. Independently Published, 2019.

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Book chapters on the topic "No-reference metrics"

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Marrugo, Andrés G., María S. Millán, Gabriel Cristóbal, Salvador Gabarda, and Héctor C. Abril. "No-reference Quality Metrics for Eye Fundus Imaging." In Computer Analysis of Images and Patterns, 486–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23672-3_59.

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Mamaev, Nikolay, Dmitry Yurin, and Andrey Krylov. "Image Ridge Denoising Using No-Reference Metric." In Advanced Concepts for Intelligent Vision Systems, 591–601. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70353-4_50.

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Molinero-Parejo, Ramón. "Geographically Weighted Methods to Validate Land Use Cover Maps." In Land Use Cover Datasets and Validation Tools, 255–65. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90998-7_13.

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AbstractOne of the most commonly used techniques for validating Land Use Cover (LUC) maps are the accuracy assessment statistics derived from the cross-tabulation matrix. However, although these accuracy metrics are applied to spatial data, this does not mean that they produce spatial results. The overall, user’s and producer’s accuracy metrics provide global information for the entire area analysed, but shed no light on possible variations in accuracy at different points within this area, a shortcoming that has been widely criticized. To address this issue, a series of techniques have been developed to integrate a spatial component into these accuracy assessment statistics for the analysis and validation of LUC maps. Geographically Weighted Regression (GWR) is a local technique for estimating the relationship between a dependent variable with respect to one or more independent variables or explanatory factors. However, unlike traditional regression techniques, it considers the distance between data points when estimating the coefficients of the regression points using a moving window. Hence, it assumes that geographic data are non-stationary i.e., they vary over space. Geographically weighted methods provide a non-stationary analysis, which can reveal the spatial relationships between reference data obtained from a LUC map and classified data. Specifically, logistic GWR is used in this chapter to estimate the accuracy of each LUC data point, so allowing us to observe the spatial variation in overall, user’s and producer’s accuracies. A specific tool (Local accuracy assessment statistics) was specially developed for this practical exercise, aimed at validating a Land Use Cover map. The Marqués de Comillas region was selected as the study area for implementing this tool and demonstrating its applicability. For the calculation of the user’s and producer’s accuracy metrics, we selected the tropical rain forest category [50] as an example. Furthermore, a series of maps were obtained by interpolating the results of the tool, so enabling a visual interpretation and a description of the spatial distribution of error and accuracy.
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Cai, Zhaowei, Qi Zhang, and Longyin Wen. "No-Reference Image Quality Metric Based on Visual Quality Saliency." In Communications in Computer and Information Science, 455–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33506-8_56.

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De, Indrajit, and Jaya Sil. "A Fuzzy Regression Analysis Based No Reference Image Quality Metric." In Advances in Intelligent Systems and Computing, 87–95. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11218-3_9.

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Galdran, Adrian, Pedro Costa, Alessandro Bria, Teresa Araújo, Ana Maria Mendonça, and Aurélio Campilho. "A No-Reference Quality Metric for Retinal Vessel Tree Segmentation." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 82–90. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00928-1_10.

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Chen, Jianhua, Yongbing Zhang, Luhong Liang, Siwei Ma, Ronggang Wang, and Wen Gao. "A No-Reference Blocking Artifacts Metric Using Selective Gradient and Plainness Measures." In Advances in Multimedia Information Processing - PCM 2008, 894–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89796-5_108.

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Charrier, Christophe, Abdelhakim Saadane, and Christine Fernandez-Maloigne. "No-Reference Learning-Based and Human Visual-Based Image Quality Assessment Metric." In Image Analysis and Processing - ICIAP 2017, 245–57. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68548-9_23.

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Xu, Jingwen, Yu Dong, Li Song, Rong Xie, Sixin Lin, and Yaqing Li. "Learning a No Reference Quality Assessment Metric for Encoded 4K-UHD Video." In Communications in Computer and Information Science, 321–30. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1194-0_28.

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Chen, Shurong, and Huijuan Jiao. "No-Reference Video Monitoring Image Blur Metric Based on Local Gradient Structure Similarity." In Artificial Intelligence and Computational Intelligence, 328–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23887-1_41.

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Conference papers on the topic "No-reference metrics"

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Gasparini, Francesca, Mirko Guarnera, Fabrizio Marini, and Raimondo Schettini. "No-reference metrics for demosaicing." In IS&T/SPIE Electronic Imaging, edited by Susan P. Farnand and Frans Gaykema. SPIE, 2010. http://dx.doi.org/10.1117/12.839952.

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Bo-Xin Zuo, Jin-Wen Tian, and De-Lie Ming. "A no-reference ringing metrics for images deconvolution." In 2008 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2008. http://dx.doi.org/10.1109/icwapr.2008.4635757.

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Hands, David, Damien Bayart, Andrew Davis, and Alex Bourret. "No reference perceptual quality metrics: approaches and limitations." In IS&T/SPIE Electronic Imaging, edited by Bernice E. Rogowitz and Thrasyvoulos N. Pappas. SPIE, 2009. http://dx.doi.org/10.1117/12.805386.

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Звездакова, Анастасия, Anastasia Zvezdakova, Дмитрий Куликов, Dmitriy Kulikov, Денис Кондранин, Denis Kondranin, Дмитрий Ватолин, and Dmitriy Vatolin. "Barriers Towards No-reference Metrics Application to Compressed Video Quality Analysis: on the Example of No-reference Metric NIQE." In 29th International Conference on Computer Graphics, Image Processing and Computer Vision, Visualization Systems and the Virtual Environment GraphiCon'2019. Bryansk State Technical University, 2019. http://dx.doi.org/10.30987/graphicon-2019-2-22-27.

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This paper analyses the application of no-reference metric NIQE to the task of video-codec comparison. A number of issues in the metric behavior on videos was detected and described. The metric has outlying scores on black and solid-colored frames. The proposed averaging technique for metric quality scores helped to improve the results in some cases. Also, NIQE has low-quality scores for videos with detailed textures and higher scores for videos of lower bit rates due to the blurring of these textures after compression. Although NIQE showed natural results for many tested videos, it is not universal and currently can’t be used for video-codec comparisons.
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Ponomarenko, Nikolay, Oleg Eremeev, Vladimir Lukin, and Karen Egiazarian. "Statistical evaluation of no-reference image visual quality metrics." In 2010 2nd European Workshop on Visual Information Processing (EUVIP). IEEE, 2010. http://dx.doi.org/10.1109/euvip.2010.5699121.

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Katsavounidis, Ioannis. "Do we Really Need No-reference Video Quality Metrics?" In MM '20: The 28th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3423328.3423502.

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Outtas, Meriem, Lu Zhang, Olivier Deforges, Wassim Hamidouche, and Amina Serir. "Evaluation of No-reference quality metrics for Ultrasound liver images." In 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2018. http://dx.doi.org/10.1109/qomex.2018.8463299.

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Zerman, Emiri, Gozde Bozdagi Akar, Baris Konuk, and Gokce Nur Yilmaz. "A comparative study on no-reference Video Quality Assessment metrics." In 2014 22nd Signal Processing and Communications Applications Conference (SIU). IEEE, 2014. http://dx.doi.org/10.1109/siu.2014.6830594.

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Marini, Fabrizio, Claudio Cusano, and Raimondo Schettini. "No-reference metrics for JPEG: analysis and refinement using wavelets." In IS&T/SPIE Electronic Imaging, edited by Susan P. Farnand and Frans Gaykema. SPIE, 2010. http://dx.doi.org/10.1117/12.839863.

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Battisti, F., M. Carli, and A. Neri. "Image forgery detection by means of no-reference quality metrics." In IS&T/SPIE Electronic Imaging, edited by Nasir D. Memon, Adnan M. Alattar, and Edward J. Delp III. SPIE, 2012. http://dx.doi.org/10.1117/12.910778.

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